A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields

Authors:
Marcos Martín;Carlos Alberola
Affiliations:
-;-
Venue:
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Year:
2002

Citing 3
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Abstract

Automatic detection of structures in medical images is of great importance for the implementation of tools that can obtain accurate measurements for an eventual diagnosis. In this paper, a new method for the creation of such tools is presented. We focus on in vivo kidney ultrasound, a target in which classical methods fail due to the inherent difficulty of such an imaging modality and organ. The proposed method operates on every slice by detecting kidney contours under a probabilistic Bayesian framework. We make use of Markov Random Fields ideas to model the problem and find the solution. A computer easy-touse interface to the model is also presented.